让大模型协作系统自动调整沟通结构，提升任务表现。
AMAS: Adaptively Determining Communication Topology for LLM-based Multi-Agent System
- 用轻量级大模型自动生成适合任务的动态通信图
- 在问答、数学推理和代码生成上均超越现有方法
- 适合需要灵活协作的大模型应用开发
尽管大语言模型（LLM）已革新自然语言处理能力，但其作为自主多智能体系统（MAS）在工业问题求解中的实际应用仍面临持续挑战。传统MAS架构受限于固定、手工设计的图拓扑，缺乏对上下文的响应能力，导致在不同学术与商业任务中效能下降。为此，我们提出AMAS，一种范式革新框架，通过新型动态图设计器重新定义基于LLM的多智能体系统。该组件通过轻量级LLM适配，自主识别任务特异的最优图结构，摆脱对单一通用结构模板的依赖。AMAS利用输入的内在特性，智能引导查询路径经由任务优化的智能体通路。在问答、数学推导和代码生成基准上的严格验证表明，AMAS在多种LLM架构下系统性超越当前最先进的单智能体与多智能体方法。研究证实，上下文感知的结构自适应是高性能LLM MAS部署的基础需求。
原文摘要 · Abstract (English)
Although large language models (LLMs) have revolutionized natural language processing capabilities, their practical implementation as autonomous multi-agent systems (MAS) for industrial problem-solving encounters persistent barriers. Conventional MAS architectures are fundamentally restricted by inflexible, hand-crafted graph topologies that lack contextual responsiveness, resulting in diminished efficacy across varied academic and commercial workloads. To surmount these constraints, we introduce AMAS, a paradigm-shifting framework that redefines LLM-based MAS through a novel dynamic graph designer. This component autonomously identifies task-specific optimal graph configurations via lightweight LLM adaptation, eliminating the reliance on monolithic, universally applied structural templates. Instead, AMAS exploits the intrinsic properties of individual inputs to intelligently direct query trajectories through task-optimized agent pathways. Rigorous validation across question answering, mathematical deduction, and code generation benchmarks confirms that AMAS systematically exceeds state-of-the-art single-agent and multi-agent approaches across diverse LLM architectures. Our investigation establishes that context-sensitive structural adaptability constitutes a foundational requirement for high-performance LLM MAS deployments.
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